Simulation Architecture for Electric Vehicle Charging Optimization in

Dresden’s Ostra District

Shangqing Wang

1,2

, Syed Irtaza Haider

1,2

, Shiwei Shen

1,2

, Faezeh Motazedian

1,2

, Rico Radeke

1,2

and

Frank H. P. Fitzek

1,2

Deutsche Telekom Chair of Communication Networks, TU Dresden, Germany

Centre for Tactile Internet with Human-in-the-Loop (CeTI), TU Dresden, Germany

ﬁ

Keywords:

Electric Vehicles (EVs), Smart Charging, Bidirectional Charging, Vehicle-to-Grid (V2G), Simulation

Architecture, Urban Mobility.

Abstract:

The integration of electric vehicles (EVs) into urban transportation systems presents signiﬁcant challenges and

opportunities for cities aiming to optimize energy usage and reduce emissions. This paper presents a simula-

tion architecture to optimize EV charging in Dresden’s Ostra District as part of the Mobilities for EU project.

The proposed architecture leverages the Simulation of Urban Mobility (SUMO) to model trafﬁc patterns and

vehicle movements, while a custom energy management system facilitates smart and bidirectional charging

capabilities. By incorporating the Amitran methodology to evaluate CO2 emissions, the architecture aims to

provide insights into the sustainability impacts of various charging strategies. The simulation environment

allows for the exploration of ”what-if” scenarios, enabling city planners and ﬂeet managers to assess the im-

plications of different charging strategies on energy consumption and grid stability. Collaboration with the

city of Dresden will be essential for validating the simulation with real data, enhancing model accuracy and

supporting informed decision-making. Ultimately, this research aims to contribute to the growing body of

knowledge on sustainable urban mobility and provide a valuable tool for optimizing EV integration in smart

cities. Future work will focus on expanding the simulation framework to include additional variables such as

renewable energy sources and user behavior patterns, further enhancing its applicability in real-world scenar-

ios.

1 INTRODUCTION

The integration of electric vehicles (EVs) into ur-

ban transportation systems presents both challenges

and opportunities for cities striving to reduce emis-

sions and optimize energy usage (Apata et al., 2023;

Mahmod et al., 2015; Wang et al., 2024b; Wang

et al., 2024a). This is particularly relevant in the

context of bidirectional charging and smart charg-

ing technologies (Vehicle-to-Grid (V2G), Vehicle-to-

Building (V2B)), which enable dynamic energy man-

agement and grid support (Wang et al., 2024a). To

validate the scalability of the project, it can integrate

with existing data in a small data set. This paper

has a goal to highlight how the architecture can con-

nect with the city of Dresden. As the adoption of

EVs continues to grow, it is crucial to develop sim-

ulation frameworks that can model the complex in-

teractions between EVs, charging infrastructure, and

the power grid. Such frameworks enable city plan-

ners and ﬂeet managers to explore various scenarios,

optimize charging strategies, and assess the impact of

EVs on the local energy system (Rehman et al., 2019;

Wang et al., 2024a).

Within the Mobilities for EU project, the Os-

tra district in Dresden, Germany, serves as an ideal

testbed for implementing and evaluating an EV charg-

ing simulation architecture. However, the current

project scope, which involves only 2-3 EVs and

charging stations, limits the observable impacts on en-

ergy savings, CO2 reduction, and green energy inte-

gration. To address this limitation, we propose de-

signing a scalable simulation architecture that, when

validated with real data from the city, can model

larger scenarios and provide meaningful insights into

the beneﬁts of EV integration. By leveraging the ca-

pabilities of SUMO and integrating it with software

for connecting EVs to charging stations, we aim to

Wang, S., Haider, S. I., Shen, S., Motazedian, F., Radeke, R. and Fitzek, F. H. P.

Simulation Architecture for Electric Vehicle Charging Optimization in Dresden’s Ostra District.

DOI: 10.5220/0013445000003953

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 14th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2025), pages 56-65

ISBN: 978-989-758-751-1; ISSN: 2184-4968

create a comprehensive simulation environment that

can address the unique challenges faced by the Ostra

district. This approach will not only facilitate the as-

sessment of current systems but also provide insights

into future developments and strategies for sustain-

able urban mobility.

This concept paper aims to explore the signiﬁ-

cance of simulation in assessing the real impacts of

V2G and smart charging technologies on urban en-

ergy systems and sustainability. By leveraging co-

simulation tools and methodologies, we will analyze

the interactions between EVs, charging infrastructure,

and the power grid. The ﬁndings will inform our ap-

proach and ensure that our simulation architecture ef-

fectively evaluates the sustainability impacts of these

technologies.

1.1 Methodology and Scalability

This section focuses on the overall methodology and

scalability. While there is a limited data set, it pro-

vides a basic framework to begin with.

• Integration of SUMO for Trafﬁc Simulation:

This allows for accurate scalable modeling of traf-

ﬁc scenarios with a limited range of EVs.

• Development of a Custom Software Model: A

model for generating optimized charging plans

that enable smart charging and peak shaving.

• Incorporation of a Systematic Approach: We

will assess CO2 emissions based on the Amitran

methodology, including a well-to-tank analysis to

evaluate both direct and indirect emissions.

• Modular Design for Easy Integration: The

modular design promotes an easy design with al-

gorithms.

• Utilization of Simulated Data: Existing stud-

ies will provide simulated data to model scenarios

such as smart charging and peak shaving, ensur-

ing alignment with sustainability goals.

• Future Collaboration: We aim to collaborate

with the city of Dresden to obtain validation, en-

hancing the accuracy of the simulation models

and supporting informed decision-making.

The resulting architecture can function as part of

the foundation architecture in the project, to integrate

existing technologies to connect with a large archi-

tecture. We aim to contribute to the growing body

of research on EV integration and smart charging

strategies in urban environments. The architecture

can serve as a valuable tool for city planners, ﬂeet

managers, and energy providers to explore the poten-

tial impacts of EVs on local transportation and en-

ergy systems, and to develop optimized solutions for

charging infrastructure and energy management.

2 BACKGROUND AND PROJECT

DESCRIPTION

2.1 Overview of the Mobilities for EU

Project

The Mobilities for EU project aims to enhance urban

mobility through innovative solutions that integrate

electric EVs with smart charging infrastructure and

advanced trafﬁc management systems. This initiative

focuses on optimizing energy consumption and reduc-

ing emissions in urban areas (MobilitiesforEU, 2024;

Barbierato et al., 2022), particularly as EV adoption

continues to grow. By aligning with broader sustain-

ability goals, the project seeks to create a more ef-

ﬁcient and environmentally friendly urban transport

system.

2.2 Simulation Architecture

Development

As part of this project, we are developing a com-

prehensive simulation architecture to model the com-

plex interactions between EVs, charging infrastruc-

ture, and the power grid. By leveraging the capabil-

ities of SUMO, we aim to create a dynamic simula-

tion environment that can replicate various trafﬁc sce-

narios and EV charging behaviors (Krajzewicz et al.,

2012; Kurczveil et al., 2014).

2.3 Importance of Simulation

Simulation architectures are crucial for understand-

ing the impacts of V2G and smart charging technolo-

gies. They enable city planners and ﬂeet managers to

explore ”what-if” scenarios—hypothetical situations

that allow for the evaluation of potential strategies

and decisions without the risks associated with real-

world implementation (Rehman et al., 2019; Stein-

brink et al., 2018). This capability is essential for con-

ducting low-cost analyses of various outcomes, help-

ing decision-makers visualize the implications of dif-

ferent actions. For instance, simulations can assess

the effects of changes in driver behavior, variations in

the number and types of EVs, modiﬁcations to charg-

ing infrastructure, and shifts in energy prices and grid

conditions (Krajzewicz et al., 2012). By simulat-

ing these scenarios, our architecture will assist ﬂeet

Simulation Architecture for Electric Vehicle Charging Optimization in Dresden’s Ostra District

managers in anticipating challenges, identifying op-

timal strategies, and making informed decisions re-

garding EV ﬂeet operations (Rehman et al., 2019).

Ultimately, the dynamic simulation environment cre-

ated with SUMO will provide critical insights (Kra-

jzewicz et al., 2012) to support the Mobilities for EU

project’s goals of optimizing energy usage and reduc-

ing emissions in urban areas. Fleet managers use V2G

strategies to optimize the overall energy output and

cost. While for the Mobilities for EU project, the city

planners are looking into scalable architecture for im-

plementation.

2.4 Co-Simulation and Integration

The components of the simulation architecture are

designed to communicate through a centralized data

exchange mechanism, ensuring synchronized opera-

tion. By employing a publish/subscribe model, the

architecture facilitates efﬁcient data sharing between

components, enabling real-time updates and dynamic

adjustments to charging strategies based on simula-

tion data. This integration will enhance our under-

standing of the interactions between EVs and charg-

ing infrastructure and their impact on urban energy

systems (Rehman et al., 2019; Rohjans et al., 2013).

Co-simulation provides a powerful framework for

studying complex systems by enabling the coupling

of multiple simulation units. This capability allows

for a comprehensive analysis of their interactions and

the exploration of various scenarios that can inform

strategic decision-making (Barbierato et al., 2022).

The insights gained from these simulations will not

only support our immediate objectives but also con-

tribute to a broader understanding of how integrated

EV systems can enhance urban mobility and sustain-

ability.

3 OBJECTIVES AND SCOPE

The primary objective of the Mobilities for EU project

is to contribute to sustainable urban mobility solutions

that improve the efﬁciency of electric vehicle (EV)

charging while supporting broader goals of reducing

carbon emissions and enhancing the livability of ur-

ban environments (MobilitiesforEU, 2024). This ini-

tiative recognizes the increasing adoption of EVs and

the necessity for innovative strategies to manage their

integration into existing infrastructure.

To achieve these objectives, we will develop a

comprehensive simulation architecture that models

the complex interactions between EVs, charging in-

frastructure, and the power grid. Initially, we will in-

corporate simulated data to model scenarios such as

smart charging and bidirectional charging, which are

essential for peak shaving and optimizing grid perfor-

mance. These scenarios will allow us to assess the

impact of various charging strategies on energy con-

sumption and grid stability.

Looking ahead, the architecture will be tested and

validated with limited city of Dresden Data. The

long-term vision is based on co-simulation architec-

ture.By utilizing a co-simulation framework, we aim

to explore urban mobility dynamics holistically, facil-

itating the assessment of current systems and inform-

ing future developments and strategies for sustainable

urban mobility.

4 ARCHITECTURAL

FOUNDATIONS

This section outlines the key methodologies and tools

chosen for our simulation architecture, justifying their

selection based on their relevance to the Mobilities for

EU project goals and their potential for scalability.

4.1 Co-Simulation Frameworks

Co-simulation is vital for analyzing complex systems,

enabling the coupling of multiple simulation units

to assess their interactions. Rehman et al. (2019)

emphasize the importance of integrating simulation

tools using High-Level Architecture (HLA), facilitat-

ing distributed simulations that allow for the evalua-

tion of interactions between EVs, charging infrastruc-

ture, and the power grid (Rehman et al., 2019). This

integration is crucial for understanding the sustain-

ability implications of these technologies, as it helps

identify strategies that optimize energy usage and re-

duce emissions. By leveraging co-simulation frame-

works, our study aims to create a more accurate rep-

resentation of EV interactions within urban environ-

ments.

4.2 Sustainability Methodology

The Amitran methodology provides a systematic

framework for evaluating the impact of information

and communication technology (ICT) measures on

CO2 emissions in the transport sector. Developed

within the European Union FP7 project Amitran, this

methodology outlines a comprehensive approach to

assess the effects of ICT on energy efﬁciency and

emissions (Mahmod et al., 2015). Our research will

adapt elements of this framework to evaluate how

SMARTGREENS 2025 - 14th International Conference on Smart Cities and Green ICT Systems

Vehicle-to-Grid (V2G) and smart charging technolo-

gies contribute to reducing CO2 emissions and en-

hancing urban sustainability. This adaptation is es-

sential for quantifying the environmental beneﬁts of

our proposed simulation architecture.

4.3 Challenges of EV Integration

Integrating EVs into urban environments presents var-

ious risks and challenges, including infrastructure

limitations and user acceptance. Apata et al. (2023)

highlight the necessity for robust simulation architec-

tures that support decision-making for urban planners

and ﬂeet managers (Apata et al., 2023). Address-

ing these challenges is essential for successful EV

integration and requires simulations that effectively

model the sustainability implications of EV adoption.

The insights from these studies directly inform our

approach to developing a simulation architecture that

considers both technical and social factors related to

EV integration.

4.4 Demand Response Strategies

Wang and Paranjape (2014) evaluate the impact of

demand response strategies on EV penetration using

multi-agent-based simulations. Their ﬁndings under-

score the importance of assessing how demand re-

sponse can inﬂuence EV charging behavior and grid

stability (Wang and Paranjape, 2014). To analyze

the effects of demand response on sustainability out-

comes, we will aim to utilize the architecture that can

allow us to analyze the effects of demand response on

sustainability outcomes, ensuring that our strategies

positively contribute to energy efﬁciency and emis-

sions reduction.

4.5 Utilizing SUMO for Trafﬁc

Simulation

SUMO is an open-source tool for simulating trafﬁc

scenarios. The latest versions also include EV mod-

els and charging behaviors. Trafﬁc Control Interface

(TraCI) uses a TCP based client/server architecture

to provide access to SUMO (Wegener et al., 2008).

Kurczveil et al. (2014) and Krajzewicz et al. (2012)

discuss how SUMO enables accurate simulation of in-

termodal trafﬁc systems, including road vehicles and

charging infrastructure (Kurczveil et al., 2014; Kra-

jzewicz et al., 2012). To enhance the realism of our

trafﬁc simulations and to improve the overall effec-

tiveness of our simulation architecture by providing

critical data on vehicle movements and charging pat-

terns. we will also be using SUMO to test our system.

In summary, these choices form the foundation of

our simulation architecture, each selected for its abil-

ity to contribute to the project’s goals and potential

for scalability. While we acknowledge the current

limitations in available data, this architecture is de-

signed to grow and adapt as the project progresses,

providing increasingly valuable insights into EV inte-

gration and sustainable urban mobility. The Amitran

methodology will be instrumental in assessing CO2

emissions reductions associated with V2G technolo-

gies. Furthermore, understanding the challenges out-

lined in existing literature will help us design a robust

simulation architecture that addresses both technical

limitations and user acceptance issues. Collectively,

these studies provide a solid foundation for develop-

ing our comprehensive simulation framework aimed

at optimizing electric vehicle charging in Dresden’s

Ostra District.

5 SIMULATION ARCHITECTURE

DESIGN

Figure 1: Simulation architecture.

The primary objective of this simulation is to ana-

lyze energy consumption and the charging dynamics

of EVs during the event. As an illustrative example,

we will simulate a scenario where vehicles arrive at

a football game, connecting to bidirectional charging

stations that allow for both charging and discharging

of energy. During the three hours of the game, we

aim to simulate the energy consumption at the sta-

dium and evaluate the total charging power over time,

comparing scenarios with and without smart charging

and bidirectional charging. By designing a scalable

simulation architecture, we can evaluate the potential

impacts of larger-scale EV integration on energy sav-

ings, CO2 reduction, and potentially renewable en-

ergy utilization.

Simulation Architecture for Electric Vehicle Charging Optimization in Dresden’s Ostra District

5.1 High-Level Architecture Design

Figure 1 presents a high-level architecture of the sim-

ulation system, illustrating the main components and

their interactions. The architecture is designed to en-

sure that all components work together seamlessly,

providing a comprehensive analysis of EV integration

in the Dresden Ostra District.

This architecture follows HLA framework, which

facilitates effective communication and integration

among simulation components, ensuring synchro-

nized operation and real-time data exchange (IEEE,

2010). By adhering to the HLA standards, we en-

hance the scalability and modularity of our simula-

tion environment, allowing for the incorporation of

various models and optimization strategies.

5.2 Key Components of the Simulation

The architecture consists of several key compo-

nents, each designed to operate independently while

communicating through a centralized data exchange

mechanism:

5.2.1 Trafﬁc Simulation with SUMO

SUMO serves as the foundational tool for simulat-

ing trafﬁc patterns and vehicle movements within the

Dresden Ostra District. It enables the simulation of

various scenarios involving the arrival and departure

of vehicles, including their routes and battery state of

charge. SUMO generates critical data for Charging

Simulation (CS), including:

• Arrival Times: Accurate modeling of vehicle

arrivals reﬂects realistic trafﬁc conditions dur-

ing events, such as football games at the Heinz-

Steyer-Stadion, essential for predicting charging

demand.

• Battery State of Charge (SoC): SUMO tracks

the SoC of each electric vehicle (EV) upon ar-

rival, allowing EMS to prioritize charging based

on individual vehicle needs, and facilitating efﬁ-

cient smart charging strategies. The change in a

vehicle’s energy content done by SUMO is deter-

mined by summing the gains in its kinetic, poten-

tial, and rotational energy from one discrete time

step to the next, and then subtracting the losses

due to various resistance factors (Kurczveil et al.,

2014).

• Route Information: By simulating vehicle

routes, SUMO provides insights into trafﬁc ﬂow

and congestion, aiding in the planning of charging

station placements and energy distribution during

peak demand.

Figure 2: SoC Fluctuations in SUMO.

The data generated by SUMO informs CS, en-

abling it to optimize charging strategies. This integra-

tion allows for the exploration of various ”what-if”

scenarios, helping city planners and ﬂeet managers

assess the implications of different charging strate-

gies on energy consumption and grid stability. As

the project progresses, collaboration with the city of

Dresden will be crucial for validating simulation out-

puts with real-world data, enhancing model accuracy

and supporting informed decision-making regarding

EV integration and charging infrastructure develop-

ment

5.2.2 Charging Simulation (CS)

CS is an important component of the simulation ar-

chitecture, designed to optimize the charging pro-

cesses of electric vehicles (EVs) based on simulated

data from SUMO. Initially, CS will operate using out-

puts such as SoC and arrival times of vehicles, which

are generated by the trafﬁc simulation. CS will con-

sist of two primary modules: smart charging and

traditional charging, each with distinct functional-

ities. The smart charging module leverages that the

data provided by SUMO to create optimized charging

schedules that consider various factors, including:

• Dynamic Charging Optimization: This module

utilizes simulated data to adjust charging times

based on predicted grid conditions, electricity

prices, and anticipated energy demand. By ana-

lyzing the SoC of incoming EVs, the system can

prioritize charging during off-peak hours or when

renewable energy generation is high, thereby re-

ducing costs and emissions (Liu et al., 2020).

• Bidirectional Charging Capabilities: The smart

charging module supports bidirectional charging

(V2G/V2B), allowing EVs to discharge energy

back into the grid during peak demand periods.

This functionality not only enhances grid stability

but also provides ﬁnancial incentives for EV own-

ers, as they can beneﬁt from selling energy back

SMARTGREENS 2025 - 14th International Conference on Smart Cities and Green ICT Systems

to the grid.

• User Preference Management: Users could in-

put their charging preferences, such as desired

departure times and minimum SoC levels. The

smart charging module ensures that these prefer-

ences are met while optimizing the overall charg-

ing strategy to align with grid conditions.

• Simulation-Based Decision Making: Initially,

the module will rely on simulated data to model

various scenarios, assessing how different charg-

ing strategies impact energy consumption and

emissions. This will allow for a comprehensive

analysis of the potential beneﬁts of smart charg-

ing technologies before real-world data is inte-

grated (Topc¸u and O

guzt

un, 2017).

Traditional Charging Module (ﬁrst-come-ﬁrst-

serve uncoordinated charging): The traditional charg-

ing module serves as a baseline scenario, where ve-

hicles charge immediately upon arrival without any

optimization. This approach enables a direct compar-

ison with the smart charging strategies, highlighting

the advantages of dynamic charging management.

5.2.3 Cost and Emissions Assessment Module

This module analyzes outputs from CS to calculate

cost saving, energy consumption, and CO2 Emis-

sions.

• Cost Saving: This component will calculate

the ﬁnancial beneﬁts achieved through optimized

charging schedules. By analyzing the charging

patterns and electricity prices, the module will

identify periods where charging can be shifted to

reduce costs, such as during off-peak hours when

electricity rates are lower. Initial results from sim-

ulated data will highlight potential cost savings

for ﬂeet managers and urban planners, demon-

strating the economic viability of smart charging

strategies.

• Energy Consumption: The module will assess

the total energy consumed by the EVs during

charging and the savings achieved through opti-

mized charging schedules. By comparing energy

usage across different charging strategies (e.g.,

traditional vs. smart charging), the module will

provide insights into how much energy can be

saved when employing renewable energy sources

or shifting charging times to align with periods

of low demand. This analysis will be supported

by data generated from CS, allowing for real-time

monitoring of energy consumption.

• CO2 Emissions: In this concept paper, we in-

troduce the plan to assess CO2 emissions reduc-

tion as part of future research. We aim to uti-

lize the Amitran methodology, which provides a

systematic approach to evaluate the environmen-

tal impact of ICT measures in the transport sec-

tor (Mahmod et al., 2015). This will involve cal-

culating emissions based on the energy mix used

for charging (e.g., the proportion of renewable en-

ergy versus fossil fuels) and the efﬁciency of the

charging process. Simulations will explore vari-

ous charging strategies and their potential impacts

on emissions, allowing for a comprehensive as-

sessment of how different approaches contribute

to sustainability goals.

5.2.4 Graphical User Interface (GUI)

The GUI facilitates user interaction and scenario man-

agement, allowing stakeholders to visualize the im-

pacts of different charging strategies on energy us-

age and emissions. Real-time feedback is provided to

users regarding optimal charging times based on grid

conditions and carbon intensity, enhancing decision-

making capabilities.

5.2.5 Communication and Data Exchange

Communication and Data Exchange The components

of the simulation architecture are designed to com-

municate through a centralized data exchange mech-

anism, ensuring synchronized operation. To facilitate

this, we utilize a Message Queuing Telemetry Trans-

port (MQTT) broker as the centerpiece of our commu-

nication strategy. MQTT is a lightweight messaging

protocol that enables efﬁcient data sharing between

components with low latency, making it ideal for real-

time applications (Yerlikaya and Dalkılıc¸, 2018).

Additionally, we implement Docker containers to

encapsulate each component of the simulation archi-

tecture. This containerization ensures that all com-

ponents can operate independently while maintaining

seamless communication through the MQTT broker.

The use of Docker allows for ﬂexible integration with

other services in the future, facilitating scalability and

modularity in our simulation environment (Paraiso

et al., 2016).

5.3 Scenario: EV Integration in the

Dresden Ostra District

This scenario explores the potential of smart charg-

ing strategies within the Dresden Ostra District, using

a high-demand event (a football game) as a concep-

tual test case. A simulated 500 vehicles are modeled.

To test the system, we assumed a percentage (70%)

for the EVs with bidirectional charging capabilities.

Simulation Architecture for Electric Vehicle Charging Optimization in Dresden’s Ostra District

The logic from the test can be used for more or less

data sets. Of the simulated 350 EVs, 50 are modeled

as not participating in the V2G/V2B program. This

allows us to test the capabilities of the system when

people will be not participating. The remaining 300

EVs participate in the simulated smart charging pro-

gram. The goal for the participation would be to test

data sharing.

The scenario focuses on the period surrounding

the football game at the Heinz-Steyer-Stadion, from

5:30 PM to the start of the game at 7 PM. The sim-

ulation is for peak savings from the integrated archi-

tecture, which is the goal of the paper. It focuses on

scalability and the ability to function, from one data

setting into another. This scenario intends to evaluate

the scaling logic model under controlled conditions,

testing the main data sets.

Figure 3: SUMO for this Scenario: The map of Dresden

was imported using osmWebWizard. The 8 Pin-points rep-

resent the starting and ending area of the vehicle. The red

circle denotes the area of interest, i.e. Ostra District. Green

vehicles are EVs and red Vehicles are fuel cars. The vehicle

size was exaggerated for visual purposes.

This simulation serves as a test run to evaluate

our current scheduling logic model and gather initial

ﬁndings. It aims to analyze energy consumption and

charging dynamics during the event. As vehicles ar-

rive, they will connect to bidirectional charging sta-

tions that allow for both charging and discharging.

The performance of the smart charging program will

be assessed against traditional charging methods to

evaluate the effectiveness of these strategies in opti-

mizing energy usage.

6 RESULTS ON DIFFERENT

SCENARIOS

This section presents the speciﬁc scenarios designed

for the simulation and outlines the approach for as-

sessing cost savings, energy consumption, and pre-

liminary ﬁndings from simulated data. The scenarios

will explore the effectiveness of smart charging strate-

gies compared to traditional charging methods.

6.1 Scenario Selection

Smart Charging vs. Traditional Charging: This

scenario will compare the energy consumption, cost

implications, and peak demand reduction of im-

plementing smart charging strategies against con-

ventional ﬁrst-come, ﬁrst-served charging methods.

The smart charging module incorporates bidirectional

charging capabilities, allowing EVs to discharge en-

ergy back into the grid during peak demand periods.

6.2 Initial Results

The simulation examines the energy dynamics at a

stadium where 350 EVs arrive randomly between 6

PM and 7 PM, with SoC levels ranging from 40%

to 80%. In this setup, 50 EVs do not participate in

vehicle-to-building (V2B) and charge their vehicles

on a ﬁrst-come, ﬁrst-served basis.

Figure 4 shows EVs’ arrival distribution and as-

sociated charging demands in each time interval. The

SUMO provides the arrival time and SoC for each EV,

allowing us to calculate the charging demand required

for all vehicles arriving within each speciﬁc time win-

dow. The blue bars represent the number of EVs arriv-

ing in each 15-minute interval, while the red line rep-

resents the energy needed to charge these EVs. The

peak arrival of EVs occurs between 6:15 PM and 6:30

PM, with over 120 EVs arriving during this time, cor-

responding to the highest energy demand of approxi-

mately 1400 kWh.

Figure 4: Arrival distribution of EVs and their associated

charging demands in each time interval.

Figure 5 shows a comparison of the stadium

baseload, the event-day baseload, the uncoordinated

EV charging demand, and the effect of peak shav-

ing using a smart charging algorithm. The base en-

ergy consumption of the stadium is represented by the

purple-shaded area, which remains relatively consis-

tent throughout the day. On event days, such as dur-

SMARTGREENS 2025 - 14th International Conference on Smart Cities and Green ICT Systems

ing a football match, the baseload increases, as indi-

cated by the yellow region, due to higher energy us-

age. The red highlighted region represents the extra

demand caused by uncoordinated EV charging using

traditional methods. This spike occurs during peak

hours in the early evening, when a signiﬁcant number

of EVs begin charging simultaneously. This uncoor-

dinated charging creates a strain on the grid, further

increasing the load during already high-demand pe-

riods. However, with smart charging strategy, repre-

sented by the green region, shows how the peak load

can be effectively ﬂattened. By shifting the charg-

ing times of EVs to off-peak periods and coordinat-

ing charging demands, the smart charging algorithm

successfully reduces the peak by approximately 20%.

Despite this peak shaving, the EVs are still able to

depart with their required SoC of 80%, ensuring that

user requirements are met while also alleviating pres-

sure on the grid.

Figure 5: Comparison of stadium baseload, event day

baseload, uncoordinated EV charging demand, and peak

shaving using smart charging.

In Figure 6 (a), the comparison between tradi-

tional and smart charging methods is presented with

a y-axis showing the aggregated EV demand. The

red line illustrates the aggregated EV demand using

traditional charging, while the blue line represents

the aggregated demand using a smart charging algo-

rithm. Under traditional charging, a signiﬁcant de-

mand spike occurs between 19:00 and 20:30, coincid-

ing with the event-day peak load, further stressing the

grid. In contrast, smart charging shifts the demand to

off-peak periods, ﬂattening the load curve and con-

tributing to peak shaving.

Figure 6 (b) represents the individual EV demand

for two randomly selected vehicles over time. The

green and purple bars show the charging plans of EV1

and EV2, revealing that during the peak demand inter-

val, both EVs are discharging for most of the duration.

Table 1 compares electricity prices and associ-

ated costs for different charging methods. It includes

the minimum, mean, and maximum electricity prices

from the grid (SPOT, 2024). The table compares

the average costs of charging using traditional and

Figure 6: (a) Comparison of aggregated EV charging de-

mand using traditional vs. smart charging, (b) Individual

charging plans of randomly selected EVs.

smart charging methods during the period from 18:00

to 22:00. With traditional charging, the average cost

is C0.184 per kWh. However, by employing smart

charging techniques, this cost is reduced to C0.170

per kWh. As shown, the average cost with smart

charging is quite close to the mean grid electricity

price for the selected duration, indicating that smart

charging can effectively optimize costs.

Table 1: Electricity Price for the period between 18:00 to

22:00.

Category Price(C/kWh)

Minimum Electricity Price 0.064

Mean Electricity Price 0.172

Maximum Electricity Price 0.264

Average Cost with Traditional Charging 0.184

Average Cost with Smart Charging 0.170

The preliminary ﬁndings from the simulated

data demonstrate the potential effectiveness of smart

charging strategies in reducing costs and optimizing

energy usage: 1. Peak demand reduction: Smart

charging successfully reduced peak load by approx-

imately 20% compared to traditional charging meth-

ods; 2. Cost savings: Smart charging reduced the av-

erage cost from C0.184/kWh to C0.170/kWh during

Simulation Architecture for Electric Vehicle Charging Optimization in Dresden’s Ostra District

the 18:00-22:00 period, a 7.6% reduction; 3. Load

ﬂattening: Smart charging shifted demand to off-peak

periods, effectively ﬂattening the load curve and re-

ducing grid stress.

These results align with the objectives of optimiz-

ing energy usage and reducing emissions by: 1. Re-

ducing strain on the grid during peak periods, poten-

tially decreasing the need for high-emission peaker

plants; 2. Enabling more efﬁcient use of existing in-

frastructure and promoting integration of renewable

energy sources.

The simulation architecture contributes to the Mo-

bilities for EU project goals by providing a plat-

form to assess both direct effects (e.g., immediate

peak reduction) and indirect effects (e.g., potential for

increased renewable energy integration) on sustain-

ability and offering insights to overcome behavioral,

functional, and market challenges in V2G implemen-

tation, as identiﬁed in the literature. This approach

allows stakeholders to evaluate and optimize V2G

strategies before real-world implementation, support-

ing the transition to more sustainable urban mobility

systems.

6.3 Future Research: CO2 Emissions

Reduction

In this concept paper, we introduce the plan to as-

sess CO2 emissions reduction as part of future re-

search. By incorporating the Amitran methodology,

we aim to evaluate the environmental impacts of dif-

ferent charging strategies, considering both direct and

indirect emissions. This analysis will provide a com-

prehensive understanding of the sustainability bene-

ﬁts of smart charging and bidirectional charging tech-

nologies.

7 DATA VALIDATION STRATEGY

AND EXPECTED

CONTRIBUTIONS

To ensure the accuracy of our simulation results, we

will implement a data validation strategy in collab-

oration with the city of Dresden. This strategy will

involve partnering with local authorities and utility

companies to access real-time data on EV usage and

charging patterns. We will integrate actual data from

the city’s EV charging infrastructure into our simula-

tion models to enhance their accuracy. Additionally,

we will validate our CO2 emissions calculations by

comparing them with actual emissions data provided

by local environmental agencies, ensuring alignment

with the Amitran methodology. The validation pro-

cess will be iterative, allowing us to reﬁne our sim-

ulation models continuously based on feedback from

real-world data. This approach will enhance the cred-

ibility of our ﬁndings and support informed strategies

for effective EV integration.

The anticipated contributions of our simulation ar-

chitecture to the ﬁeld of urban mobility and electric

vehicle integration are signiﬁcant. Our framework

will provide city planners and ﬂeet managers with

a robust tool to evaluate the impacts of various EV

charging strategies on urban energy systems. Fur-

thermore, we will offer valuable insights into the sus-

tainability beneﬁts of smart and bidirectional charg-

ing technologies, including emissions reductions. Our

research will also inform urban planning and policy

decisions regarding EV infrastructure by identifying

optimal charging strategies. Finally, we aim to ﬁll ex-

isting gaps in the literature on EV integration and sus-

tainable urban mobility by leveraging co-simulation

techniques and the Amitran methodology.

8 CONCLUSIONS

In this paper, we presented a simulation architecture

designed to optimize electric vehicle (EV) charging

in Dresden’s Ostra District as part of the Mobilities

for EU project. Our approach leverages co-simulation

frameworks, speciﬁcally utilizing the Simulation of

Urban Mobility (SUMO) and the High-Level Archi-

tecture (HLA), to model the complex interactions be-

tween EVs, charging infrastructure, and the power

grid. Through this integration, we aim to facilitate

real-time data exchange and visualization, allowing

for a deeper understanding of how these systems in-

teract and impact urban energy dynamics.

The ﬁndings from our simulation architecture will

provide valuable insights into various charging strate-

gies, enabling city planners and ﬂeet managers to

make informed decisions that enhance energy efﬁ-

ciency and reduce emissions. By evaluating scenarios

involving smart charging and bidirectional charging

technologies, we anticipate identifying optimal strate-

gies for peak shaving and improving grid stability.

Moreover, our research highlights the importance

of collaboration with local authorities in validating

simulation models with real data, which will further

enhance the accuracy and applicability of our ﬁnd-

ings. The insights gained from this work will con-

tribute to the growing body of knowledge on sustain-

able urban mobility, providing a robust framework for

future studies aimed at integrating EVs into urban en-

vironments.

SMARTGREENS 2025 - 14th International Conference on Smart Cities and Green ICT Systems

Ultimately, this research not only addresses the

immediate challenges associated with EV integration

but also lays the groundwork for future advancements

in smart charging solutions. As cities continue to

adopt EVs as a means of reducing carbon emissions

and promoting sustainable transportation, our simula-

tion architecture will serve as a critical tool in shaping

effective policies and strategies that support the tran-

sition toward greener urban mobility.

ACKNOWLEDGEMENT

This work is funded by the European Union under

Grant Agreement No 101139666, MOBILITIES FOR

EU. Views and opinions expressed are those of the au-

thors only and do not necessarily reﬂect those of the

European Union or the European Climate, Infrastruc-

ture and Environment Executive Agency (CINEA).

Neither the European Union nor the granting author-

ity can be held responsible for them. This work is

supported by the German Federal Ministry for Eco-

nomic Affairs and Climate Action (BMWK) under

project ID 03EI6082A, DymoBat, the German Re-

search Foundation (DFG) as part of Germany’s Ex-

cellence Strategy—EXC 2050/1—Cluster of Excel-

lence “Centre for Tactile Internet with Human-in-the-

Loop” (CeTI) of Technische Universit

at Dresden un-

der project ID 390696704 and the Federal Ministry

for Education and Research (BMBF) in the program

of “Souver

an. Digital. Vernetzt.” Joint project 6G-

life, grant number 16KISK001K.

REFERENCES

Apata, O., Bokoro, P. N., and Sharma, G. (2023). The

risks and challenges of electric vehicle integration into

smart cities. Energies, 16(14):5274.

Barbierato, L., Mazzarino, P. R., Montarolo, M., Macii, A.,

Patti, E., and Bottaccioli, L. (2022). A comparison

study of co-simulation frameworks for multi-energy

systems: The scalability problem. Energy Informatics,

5(Suppl 4):53.

IEEE (2010). Ieee standard for modeling and simulation

(m&s) high level architecture (hla)–object model tem-

plate (omt) speciﬁcation. IEEE Std 1516.2-2010 (Re-

vision of IEEE Std 1516.2-2000).

Krajzewicz, D., Erdmann, J., Behrisch, M., and Bieker,

L. (2012). Recent development and applications of

sumo–simulation of urban mobility. International

Journal on Advances in Systems and Measurements,

5(3&4).

Kurczveil, T., L

opez, P.

A., and Schnieder, E. (2014). Im-

plementation of an energy model and a charging in-

frastructure in sumo. In Simulation of Urban Mobility:

SUMO 2013 Conference, pages 33–43. Springer.

Liu, W., Shi, X., Zhao, J., Zhang, X.-P., and Xue, Y. (2020).

Electric vehicle charging simulation framework con-

sidering trafﬁc, user, and power grid. Journal of Mod-

ern Power Systems and Clean Energy, 9(3):602–611.

Mahmod, M., Jonkers, E., Klunder, G. A., Benz, T., and

Winder, A. (2015). Amitran methodology framework

for evaluating the impact of ict-based measures on co2

emissions in the transport ﬁeld. IET Intelligent Trans-

port Systems, 9(4):418–428.

MobilitiesforEU (2024). Mobilities for eu. https://

mobilities-for.eu/. EU Horizon Project.

Paraiso, F., Challita, S., Al-Dhuraibi, Y., and Merle, P.

(2016). Model-driven management of docker contain-

ers. In 2016 IEEE 9th International Conference on

Cloud Computing (CLOUD), pages 718–725. IEEE.

Rehman, K., Kipouridis, O., Karnouskos, S., Frendo, O.,

Dickel, H., Lipps, J., and Verzano, N. (2019). A

cloud-based development environment using hla and

kubernetes for the co-simulation of a corporate elec-

tric vehicle ﬂeet. In 2019 IEEE/SICE International

Symposium on System Integration (SII), pages 47–54.

IEEE.

Rohjans, S., Lehnhoff, S., Sch

utte, S., Scherfke, S., and

Hussain, S. (2013). mosaik: A modular platform for

the evaluation of agent-based smart grid control. In

IEEE PES ISGT Europe 2013, pages 1–5. IEEE.

SPOT, E. (2024). Day-ahead auction prices for

germany-luxembourg. https://www.epexspot.com/en/

market-data. Accessed: 2024-09-16.

Steinbrink, C., van der Meer, A. A., Cvetkovic, M.,

Babazadeh, D., Rohjans, S., Palensky, P., and Lehn-

hoff, S. (2018). Smart grid co-simulation with mo-

saik and hla: A comparison study. Computer Science-

Research and Development, 33:135–143.

Topc¸u, O. and O

guzt

un, H. (2017). Guide to Distributed

Simulation with HLA. Springer International Publish-

ing, Cham, Switzerland, 1st edition.

Wang, S., Cabrera, J. A., and Fitzek, F. H. P. (2024a).

Bidirectional charging use cases: Innovations in e-

mobility and power-grid ﬂexibility. In IEEE Inter-

national Smart Cities Conference (ISC2), pages 1–6,

Pattaya, Thailand.

Wang, S., Lehmann, C., Radeke, R., and Fitzek, F. H. P.

(2024b). Enabling sustainable urban mobility: The

role of 5g communication in the mobilities for eu

project. In IEEE International Smart Cities Confer-

ence (ISC2), pages 1–6, Pattaya, Thailand.

Wang, Z. and Paranjape, R. (2014). An evaluation of elec-

tric vehicle penetration under demand response in a

multi-agent based simulation. In 2014 IEEE Elec-

trical Power and Energy Conference (EPEC), pages

220–225. IEEE.

Wegener, A., Pi

orkowski, M., Raya, M., Hellbr

uck, H.,

Fischer, S., and Hubaux, J.-P. (2008). Traci: An in-

terface for coupling road trafﬁc and network simula-

tors. In Proceedings of the 11th Communications and

Networking Simulation Symposium, pages 155–163.

ACM.

Yerlikaya,

O. and Dalkılıc¸, G. (2018). Authentication and

authorization mechanism on mqtt protocol. In 2018

3rd International Conference on Computer Science

and Engineering (UBMK), pages 145–150. IEEE.

Simulation Architecture for Electric Vehicle Charging Optimization in Dresden’s Ostra District